This project will develop a comprehensive array of new statistical methods for analyzing genome-wide association studies, and will apply these methods, and other appropriate methods, to perform in-depth analyses of NIH-funded association studies that attempt to unravel the genetic basis of common complex diseases. The overall objective is for the work to produce, and enable others to produce, discoveries and insights that aid the development of medical diagnostic tests, more effective therapies, and, ultimately, prevention of disease. Our focus will be primarily on developing new Bayesian statistical methods, which complement and improve on existing analysis approaches. The specific aims include the refinement of existing Bayesian statistical approaches to assessing correlation between genotype and quantitative phenotype to improve their robustness to deviations from underlying modeling assumptions;extension of these methods to allow analysis of binary (case/control) phenotypes, and family-based designs;and modification of these approaches to incorporate relevant biological prior information (e.g.~information on molecular pathways). The result will be a suite of tools, implemented in user-friendly software, for performing both single-marker and multi-marker analyses for many of the most commonly-used association study designs, including both quantitative and binary (case/control) phenotypes for population samples and parent-offspring trios.